Specification Error is defined as a situation where one or more key feature, variable or assumption of a statistical model is not correct. Specification is the process of developing the statistical model in a regression analysis. Copy the link given below and paste it in new browser window to get more information on Specification Error:- http://www.transtutors.com/homework-help/economics/specification-errors.aspx

1. Specification Error

2. Definition and types of specification
error
• Specification errors in regression are the errors that occur
because of a mistake in one of the variables or other assumptions
of the model
• A regression model will have a specification error when at least
one of the following problems occur in that model:
1. Inclusion of irrelevant variable
2. Omission of relevant variable
3. Incorrect functional form

3. Inclusion of irrelevant variable
• This is the least serious problem that leads to specification error
• The hypothesis tests of a model which has included an irrelevant
variable are still valid
• The inclusion of irrelevant variable does not affect the
relationship between other variables and the dependent variable
because the estimator for such a variable turns out to be zero
• The estimators of such a model are unbiased and consistent
• However, the estimators are not efficient because the variances
are larger than they would have been in the model excluding the
irrelevant variable
• The estimators violate the BLUE (Best Linear Unbiased Estimator)
concept of regression because they are inefficient

4. Omission of relevant variable
• Omission of a relevant variable has serious consequences for the
regression analysis and almost everything goes wrong in this case
• The estimators are biased and inconsistent
• As a result the hypothesis tests do not hold
• Even choosing a larger sample size does not make the estimators
unbiased or consistent
• The inconsistency of estimators is generated by a lower than
normal variance in the regression analysis

5. Incorrect functional form and
measurement errors
• When you choose the wrong functional form for your regression
model, the model will have a specification error
• For example, if you choose a double log model for your analysis
instead of the log-liner model (which describes the relation
between the independent and dependent variables better) your
model will suffer from a specification bias
• Measurement errors are the errors that occur in measuring the
magnitude of the variables and this too leads to larger variances
for the model than there would have been if there were no
measurement error

6. Tests for checking for the presence
of specification errors
• Given that specification errors lead to problems for the regression
analysis, it is very important to check for these errors when we
develop our model
• F-test and t-test have been recommended by Gujrati but it is not
advisable to use these tests for checking for the presence of
specification errors
• RESET is a test developed by J B Ramsey, a famous
econometrician and has been gaining popularity
• Other tests include Likelihood ratio test and Lagrange Multiplier
test
• One should run these tests on their models to ensure that there
are no specification errors in the model so that they have a robust
regression model

7. Helpful links
• You can go through the steps for RESET test here:
https://www.uvm.edu/~wgibson/Classes/200f09/Technical_n
otes/Ramsey_RESET.pdf
• These lecture notes will help in understanding the concept and
consequences of specification errors in detail:
http://ocw.uc3m.es/economia/econometrics/lecture-notes-
1/Topic5_logo.pdf/at_download/file

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